Overview

Dataset statistics

Number of variables32
Number of observations5500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory288.0 B

Variable types

Categorical12
Numeric20

Alerts

device_fraud_count has constant value "0"Constant
foreign_request is highly imbalanced (76.8%)Imbalance
source is highly imbalanced (92.8%)Imbalance
device_distinct_emails_8w is highly imbalanced (80.3%)Imbalance
name_email_similarity has unique valuesUnique
velocity_6h has unique valuesUnique
velocity_24h has unique valuesUnique
velocity_4w has unique valuesUnique
bank_branch_count_8w has 1018 (18.5%) zerosZeros
employment_status has 4203 (76.4%) zerosZeros
housing_status has 2093 (38.1%) zerosZeros
month has 734 (13.3%) zerosZeros

Reproduction

Analysis started2023-07-17 08:04:24.475252
Analysis finished2023-07-17 08:05:13.569157
Duration49.09 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

fraud_bool
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
2786 
1
2714 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

Length

2023-07-17T14:35:13.631053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:13.728632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

Most occurring characters

ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2786
50.7%
1 2714
49.3%

income
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61836364
Minimum0.1
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:13.799670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median0.7
Q30.9
95-th percentile0.9
Maximum0.9
Range0.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.28821972
Coefficient of variation (CV)0.4661007
Kurtosis-1.0046293
Mean0.61836364
Median Absolute Deviation (MAD)0.2
Skewness-0.66584812
Sum3401
Variance0.083070608
MonotonicityNot monotonic
2023-07-17T14:35:13.887862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.9 1778
32.3%
0.8 769
14.0%
0.1 710
 
12.9%
0.6 547
 
9.9%
0.7 540
 
9.8%
0.4 357
 
6.5%
0.2 305
 
5.5%
0.5 282
 
5.1%
0.3 212
 
3.9%
ValueCountFrequency (%)
0.1 710
 
12.9%
0.2 305
 
5.5%
0.3 212
 
3.9%
0.4 357
 
6.5%
0.5 282
 
5.1%
0.6 547
 
9.9%
0.7 540
 
9.8%
0.8 769
14.0%
0.9 1778
32.3%
ValueCountFrequency (%)
0.9 1778
32.3%
0.8 769
14.0%
0.7 540
 
9.8%
0.6 547
 
9.9%
0.5 282
 
5.1%
0.4 357
 
6.5%
0.3 212
 
3.9%
0.2 305
 
5.5%
0.1 710
 
12.9%

name_email_similarity
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44950882
Minimum0.00033963888
Maximum0.99995321
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:14.003127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.00033963888
5-th percentile0.056198743
Q10.16953128
median0.41734135
Q30.72415984
95-th percentile0.90509819
Maximum0.99995321
Range0.99961357
Interquartile range (IQR)0.55462856

Descriptive statistics

Standard deviation0.29549148
Coefficient of variation (CV)0.65736526
Kurtosis-1.3286916
Mean0.44950882
Median Absolute Deviation (MAD)0.2706613
Skewness0.21908755
Sum2472.2985
Variance0.087315218
MonotonicityNot monotonic
2023-07-17T14:35:14.122339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1135986114 1
 
< 0.1%
0.8473376745 1
 
< 0.1%
0.1703692725 1
 
< 0.1%
0.7496820908 1
 
< 0.1%
0.4120443429 1
 
< 0.1%
0.3590355492 1
 
< 0.1%
0.4213629425 1
 
< 0.1%
0.607641557 1
 
< 0.1%
0.7466242621 1
 
< 0.1%
0.535549258 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
0.0003396388756 1
< 0.1%
0.0004115615811 1
< 0.1%
0.001378701092 1
< 0.1%
0.00161019191 1
< 0.1%
0.002719439667 1
< 0.1%
0.002736811317 1
< 0.1%
0.003126485944 1
< 0.1%
0.003246608194 1
< 0.1%
0.003498198554 1
< 0.1%
0.003624020878 1
< 0.1%
ValueCountFrequency (%)
0.9999532132 1
< 0.1%
0.9999132357 1
< 0.1%
0.9999059217 1
< 0.1%
0.9998552165 1
< 0.1%
0.9997802 1
< 0.1%
0.9997214691 1
< 0.1%
0.9996965367 1
< 0.1%
0.9995439576 1
< 0.1%
0.999534157 1
< 0.1%
0.9995267597 1
< 0.1%

prev_address_months_count
Real number (ℝ)

Distinct204
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.924545
Minimum-1
Maximum350
Zeros0
Zeros (%)0.0%
Negative4504
Negative (%)81.9%
Memory size85.9 KiB
2023-07-17T14:35:14.242037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q3-1
95-th percentile89.05
Maximum350
Range351
Interquartile range (IQR)0

Descriptive statistics

Standard deviation41.017855
Coefficient of variation (CV)3.4397835
Kurtosis26.43965
Mean11.924545
Median Absolute Deviation (MAD)0
Skewness4.7522581
Sum65585
Variance1682.4644
MonotonicityNot monotonic
2023-07-17T14:35:14.367095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 4504
81.9%
11 41
 
0.7%
29 33
 
0.6%
10 32
 
0.6%
32 29
 
0.5%
28 28
 
0.5%
31 28
 
0.5%
12 27
 
0.5%
30 26
 
0.5%
27 25
 
0.5%
Other values (194) 727
 
13.2%
ValueCountFrequency (%)
-1 4504
81.9%
7 1
 
< 0.1%
8 6
 
0.1%
9 18
 
0.3%
10 32
 
0.6%
11 41
 
0.7%
12 27
 
0.5%
13 20
 
0.4%
14 3
 
0.1%
15 1
 
< 0.1%
ValueCountFrequency (%)
350 1
< 0.1%
337 2
< 0.1%
336 1
< 0.1%
334 1
< 0.1%
333 1
< 0.1%
332 2
< 0.1%
330 1
< 0.1%
326 2
< 0.1%
324 1
< 0.1%
323 2
< 0.1%
Distinct373
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.36891
Minimum-1
Maximum387
Zeros40
Zeros (%)0.7%
Negative12
Negative (%)0.2%
Memory size85.9 KiB
2023-07-17T14:35:14.494815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5
Q136
median75
Q3146
95-th percentile287.05
Maximum387
Range388
Interquartile range (IQR)110

Descriptive statistics

Standard deviation88.418081
Coefficient of variation (CV)0.87224063
Kurtosis1.0653166
Mean101.36891
Median Absolute Deviation (MAD)50
Skewness1.2186786
Sum557529
Variance7817.7571
MonotonicityNot monotonic
2023-07-17T14:35:14.612159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 72
 
1.3%
6 72
 
1.3%
4 58
 
1.1%
8 54
 
1.0%
10 52
 
0.9%
39 50
 
0.9%
52 50
 
0.9%
9 50
 
0.9%
1 50
 
0.9%
3 49
 
0.9%
Other values (363) 4943
89.9%
ValueCountFrequency (%)
-1 12
 
0.2%
0 40
0.7%
1 50
0.9%
2 46
0.8%
3 49
0.9%
4 58
1.1%
5 72
1.3%
6 72
1.3%
7 48
0.9%
8 54
1.0%
ValueCountFrequency (%)
387 2
 
< 0.1%
386 2
 
< 0.1%
385 2
 
< 0.1%
383 3
0.1%
382 2
 
< 0.1%
381 2
 
< 0.1%
380 5
0.1%
379 3
0.1%
378 2
 
< 0.1%
377 5
0.1%

customer_age
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.483636
Minimum10
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:14.715969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q130
median40
Q350
95-th percentile60
Maximum90
Range80
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.106177
Coefficient of variation (CV)0.34965063
Kurtosis-0.28766018
Mean37.483636
Median Absolute Deviation (MAD)10
Skewness0.30854397
Sum206160
Variance171.77187
MonotonicityNot monotonic
2023-07-17T14:35:14.801798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
30 1447
26.3%
40 1390
25.3%
50 1131
20.6%
20 963
17.5%
60 391
 
7.1%
10 81
 
1.5%
70 70
 
1.3%
80 26
 
0.5%
90 1
 
< 0.1%
ValueCountFrequency (%)
10 81
 
1.5%
20 963
17.5%
30 1447
26.3%
40 1390
25.3%
50 1131
20.6%
60 391
 
7.1%
70 70
 
1.3%
80 26
 
0.5%
90 1
 
< 0.1%
ValueCountFrequency (%)
90 1
 
< 0.1%
80 26
 
0.5%
70 70
 
1.3%
60 391
 
7.1%
50 1131
20.6%
40 1390
25.3%
30 1447
26.3%
20 963
17.5%
10 81
 
1.5%

days_since_request
Real number (ℝ)

Distinct5499
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94593694
Minimum2.697941 × 10-6
Maximum75.829756
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:14.917843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.697941 × 10-6
5-th percentile0.0012692287
Q10.0063032418
median0.013979299
Q30.024789744
95-th percentile3.4570179
Maximum75.829756
Range75.829754
Interquartile range (IQR)0.018486503

Descriptive statistics

Standard deviation5.3517391
Coefficient of variation (CV)5.6576067
Kurtosis121.96545
Mean0.94593694
Median Absolute Deviation (MAD)0.0086620483
Skewness10.12379
Sum5202.6532
Variance28.641112
MonotonicityNot monotonic
2023-07-17T14:35:15.030992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01470116746 2
 
< 0.1%
0.01740596538 1
 
< 0.1%
0.01004920826 1
 
< 0.1%
0.01177630498 1
 
< 0.1%
0.01854820594 1
 
< 0.1%
0.01167674908 1
 
< 0.1%
0.008625558209 1
 
< 0.1%
0.004703332796 1
 
< 0.1%
0.01612254574 1
 
< 0.1%
0.03701241689 1
 
< 0.1%
Other values (5489) 5489
99.8%
ValueCountFrequency (%)
2.697941004 × 10-61
< 0.1%
8.394471607 × 10-61
< 0.1%
8.999457208 × 10-61
< 0.1%
9.11273222 × 10-61
< 0.1%
1.259373091 × 10-51
< 0.1%
1.569768857 × 10-51
< 0.1%
2.032235059 × 10-51
< 0.1%
2.349440459 × 10-51
< 0.1%
2.513921638 × 10-51
< 0.1%
2.530129766 × 10-51
< 0.1%
ValueCountFrequency (%)
75.82975624 1
< 0.1%
75.49592131 1
< 0.1%
73.92562466 1
< 0.1%
72.92373743 1
< 0.1%
72.60659273 1
< 0.1%
71.98312145 1
< 0.1%
71.85110989 1
< 0.1%
71.84278559 1
< 0.1%
71.74582649 1
< 0.1%
71.55532456 1
< 0.1%

intended_balcon_amount
Real number (ℝ)

Distinct5499
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1568069
Minimum-11.740377
Maximum109.86996
Zeros0
Zeros (%)0.0%
Negative4486
Negative (%)81.6%
Memory size85.9 KiB
2023-07-17T14:35:15.149492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-11.740377
5-th percentile-1.5822908
Q1-1.1807301
median-0.86538374
Q3-0.45194807
95-th percentile49.62118
Maximum109.86996
Range121.61033
Interquartile range (IQR)0.72878201

Descriptive statistics

Standard deviation18.106884
Coefficient of variation (CV)2.9409537
Kurtosis10.426904
Mean6.1568069
Median Absolute Deviation (MAD)0.35326368
Skewness3.0649575
Sum33862.438
Variance327.85925
MonotonicityNot monotonic
2023-07-17T14:35:15.261677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.480971648 2
 
< 0.1%
-1.546016419 1
 
< 0.1%
32.01209322 1
 
< 0.1%
-0.631390477 1
 
< 0.1%
-0.7185656382 1
 
< 0.1%
-1.771774697 1
 
< 0.1%
-0.2243696952 1
 
< 0.1%
-0.9623697907 1
 
< 0.1%
-0.9067047202 1
 
< 0.1%
-0.9935020381 1
 
< 0.1%
Other values (5489) 5489
99.8%
ValueCountFrequency (%)
-11.74037717 1
< 0.1%
-9.143508412 1
< 0.1%
-8.289799918 1
< 0.1%
-6.264140809 1
< 0.1%
-5.707535278 1
< 0.1%
-5.27927063 1
< 0.1%
-3.966403523 1
< 0.1%
-3.757374772 1
< 0.1%
-2.147438874 1
< 0.1%
-2.107363829 1
< 0.1%
ValueCountFrequency (%)
109.8699573 1
< 0.1%
108.7857489 1
< 0.1%
108.5765987 1
< 0.1%
108.5554612 1
< 0.1%
108.4014015 1
< 0.1%
108.1021321 1
< 0.1%
107.4991144 1
< 0.1%
107.4714841 1
< 0.1%
107.3930297 1
< 0.1%
106.7108388 1
< 0.1%

payment_type
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
2056 
2
1745 
0
1048 
3
651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

Length

2023-07-17T14:35:15.369268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:15.471003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

Most occurring characters

ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2056
37.4%
2 1745
31.7%
0 1048
19.1%
3 651
 
11.8%

zip_count_4w
Real number (ℝ)

Distinct2569
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1603.7898
Minimum18
Maximum6027
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:15.849637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile508.95
Q1909
median1297
Q32010
95-th percentile3636
Maximum6027
Range6009
Interquartile range (IQR)1101

Descriptive statistics

Standard deviation1009.1014
Coefficient of variation (CV)0.62919803
Kurtosis1.9585907
Mean1603.7898
Median Absolute Deviation (MAD)475
Skewness1.4004074
Sum8820844
Variance1018285.6
MonotonicityNot monotonic
2023-07-17T14:35:15.954262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
994 12
 
0.2%
1227 11
 
0.2%
1007 10
 
0.2%
822 10
 
0.2%
1159 9
 
0.2%
728 9
 
0.2%
885 9
 
0.2%
971 9
 
0.2%
1466 9
 
0.2%
1065 9
 
0.2%
Other values (2559) 5403
98.2%
ValueCountFrequency (%)
18 1
< 0.1%
29 1
< 0.1%
67 1
< 0.1%
75 1
< 0.1%
90 1
< 0.1%
92 1
< 0.1%
94 1
< 0.1%
99 1
< 0.1%
103 1
< 0.1%
105 1
< 0.1%
ValueCountFrequency (%)
6027 1
< 0.1%
6013 1
< 0.1%
6007 1
< 0.1%
5982 1
< 0.1%
5921 1
< 0.1%
5905 1
< 0.1%
5894 1
< 0.1%
5836 1
< 0.1%
5812 2
< 0.1%
5785 1
< 0.1%

velocity_6h
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5413.6388
Minimum68.669243
Maximum16200.292
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:16.069251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum68.669243
5-th percentile1169.0714
Q13117.4214
median5075.3105
Q37457.5817
95-th percentile10707.053
Maximum16200.292
Range16131.623
Interquartile range (IQR)4340.1603

Descriptive statistics

Standard deviation2994.3219
Coefficient of variation (CV)0.55310707
Kurtosis0.087750437
Mean5413.6388
Median Absolute Deviation (MAD)2171.8366
Skewness0.59264513
Sum29775013
Variance8965963.4
MonotonicityNot monotonic
2023-07-17T14:35:16.184088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12185.50478 1
 
< 0.1%
8086.837173 1
 
< 0.1%
318.5248167 1
 
< 0.1%
3921.554639 1
 
< 0.1%
7849.431112 1
 
< 0.1%
4512.733724 1
 
< 0.1%
4717.713655 1
 
< 0.1%
1686.345876 1
 
< 0.1%
1730.932311 1
 
< 0.1%
5766.983778 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
68.66924336 1
< 0.1%
161.6651232 1
< 0.1%
177.1771203 1
< 0.1%
179.6870234 1
< 0.1%
194.1549504 1
< 0.1%
197.2774141 1
< 0.1%
206.4354988 1
< 0.1%
221.702255 1
< 0.1%
230.8654592 1
< 0.1%
281.8406927 1
< 0.1%
ValueCountFrequency (%)
16200.29222 1
< 0.1%
15474.98509 1
< 0.1%
15415.78402 1
< 0.1%
15345.08195 1
< 0.1%
15270.06753 1
< 0.1%
15128.94742 1
< 0.1%
15127.60534 1
< 0.1%
15080.81441 1
< 0.1%
15069.02037 1
< 0.1%
15055.29609 1
< 0.1%

velocity_24h
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4694.7363
Minimum1440.5683
Maximum9307.0121
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:16.313791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1440.5683
5-th percentile2491.9882
Q13503.8754
median4699.966
Q35681.7254
95-th percentile7253.0797
Maximum9307.0121
Range7866.4438
Interquartile range (IQR)2177.8501

Descriptive statistics

Standard deviation1463.5054
Coefficient of variation (CV)0.31173325
Kurtosis-0.33864234
Mean4694.7363
Median Absolute Deviation (MAD)1080.023
Skewness0.32122
Sum25821050
Variance2141848.1
MonotonicityNot monotonic
2023-07-17T14:35:16.433340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6087.249578 1
 
< 0.1%
4908.099607 1
 
< 0.1%
3058.052477 1
 
< 0.1%
3250.583422 1
 
< 0.1%
3602.880047 1
 
< 0.1%
4849.808632 1
 
< 0.1%
4596.579274 1
 
< 0.1%
5735.483006 1
 
< 0.1%
5557.298205 1
 
< 0.1%
3646.737932 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
1440.568313 1
< 0.1%
1470.581521 1
< 0.1%
1641.734061 1
< 0.1%
1656.756847 1
< 0.1%
1662.85679 1
< 0.1%
1683.979983 1
< 0.1%
1721.157819 1
< 0.1%
1722.800523 1
< 0.1%
1724.914174 1
< 0.1%
1732.693905 1
< 0.1%
ValueCountFrequency (%)
9307.012145 1
< 0.1%
9172.107906 1
< 0.1%
9146.618796 1
< 0.1%
9145.979832 1
< 0.1%
9136.625743 1
< 0.1%
9133.950541 1
< 0.1%
9082.798542 1
< 0.1%
9065.243221 1
< 0.1%
9065.118275 1
< 0.1%
9049.808917 1
< 0.1%

velocity_4w
Real number (ℝ)

Distinct5500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4808.57
Minimum2967.3765
Maximum6919.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:16.558885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2967.3765
5-th percentile3107.8514
Q14231.3696
median4870.2859
Q35449.6539
95-th percentile6600.6224
Maximum6919.17
Range3951.7935
Interquartile range (IQR)1218.2842

Descriptive statistics

Standard deviation949.37744
Coefficient of variation (CV)0.19743446
Kurtosis-0.44254466
Mean4808.57
Median Absolute Deviation (MAD)620.92462
Skewness0.024825132
Sum26447135
Variance901317.52
MonotonicityNot monotonic
2023-07-17T14:35:16.677233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5990.732491 1
 
< 0.1%
5334.987836 1
 
< 0.1%
3326.265866 1
 
< 0.1%
3467.2048 1
 
< 0.1%
6088.534971 1
 
< 0.1%
6720.204893 1
 
< 0.1%
4422.437518 1
 
< 0.1%
3884.803713 1
 
< 0.1%
4870.057854 1
 
< 0.1%
3140.502201 1
 
< 0.1%
Other values (5490) 5490
99.8%
ValueCountFrequency (%)
2967.376507 1
< 0.1%
2986.195447 1
< 0.1%
3003.744211 1
< 0.1%
3004.670722 1
< 0.1%
3008.456035 1
< 0.1%
3009.326734 1
< 0.1%
3013.061076 1
< 0.1%
3015.730706 1
< 0.1%
3017.090116 1
< 0.1%
3020.801253 1
< 0.1%
ValueCountFrequency (%)
6919.170045 1
< 0.1%
6880.285858 1
< 0.1%
6871.180277 1
< 0.1%
6850.283342 1
< 0.1%
6849.30575 1
< 0.1%
6849.134853 1
< 0.1%
6844.282607 1
< 0.1%
6842.196871 1
< 0.1%
6839.195877 1
< 0.1%
6838.784656 1
< 0.1%

bank_branch_count_8w
Real number (ℝ)

Distinct692
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157.21709
Minimum0
Maximum2306
Zeros1018
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:16.799468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q318
95-th percentile1404.1
Maximum2306
Range2306
Interquartile range (IQR)17

Descriptive statistics

Standard deviation431.24321
Coefficient of variation (CV)2.7429792
Kurtosis8.4792649
Mean157.21709
Median Absolute Deviation (MAD)7
Skewness3.0661601
Sum864694
Variance185970.71
MonotonicityNot monotonic
2023-07-17T14:35:16.911074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1025
18.6%
0 1018
18.5%
2 352
 
6.4%
11 172
 
3.1%
12 159
 
2.9%
10 149
 
2.7%
13 146
 
2.7%
8 132
 
2.4%
9 125
 
2.3%
7 123
 
2.2%
Other values (682) 2099
38.2%
ValueCountFrequency (%)
0 1018
18.5%
1 1025
18.6%
2 352
 
6.4%
3 72
 
1.3%
4 69
 
1.3%
5 62
 
1.1%
6 89
 
1.6%
7 123
 
2.2%
8 132
 
2.4%
9 125
 
2.3%
ValueCountFrequency (%)
2306 1
< 0.1%
2266 1
< 0.1%
2258 1
< 0.1%
2209 1
< 0.1%
2182 1
< 0.1%
2179 1
< 0.1%
2165 1
< 0.1%
2152 1
< 0.1%
2148 1
< 0.1%
2147 1
< 0.1%
Distinct34
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.4185455
Minimum0
Maximum36
Zeros22
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:17.015331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q15
median8
Q311
95-th percentile18
Maximum36
Range36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.0553314
Coefficient of variation (CV)0.60049939
Kurtosis0.69141167
Mean8.4185455
Median Absolute Deviation (MAD)3
Skewness0.8210484
Sum46302
Variance25.556375
MonotonicityNot monotonic
2023-07-17T14:35:17.119345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7 469
 
8.5%
5 465
 
8.5%
8 460
 
8.4%
6 428
 
7.8%
2 401
 
7.3%
4 375
 
6.8%
9 373
 
6.8%
11 330
 
6.0%
3 329
 
6.0%
10 306
 
5.6%
Other values (24) 1564
28.4%
ValueCountFrequency (%)
0 22
 
0.4%
1 191
3.5%
2 401
7.3%
3 329
6.0%
4 375
6.8%
5 465
8.5%
6 428
7.8%
7 469
8.5%
8 460
8.4%
9 373
6.8%
ValueCountFrequency (%)
36 1
 
< 0.1%
32 2
 
< 0.1%
31 2
 
< 0.1%
30 2
 
< 0.1%
29 2
 
< 0.1%
28 3
 
0.1%
27 3
 
0.1%
26 4
 
0.1%
25 7
0.1%
24 11
0.2%

employment_status
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.48727273
Minimum0
Maximum6
Zeros4203
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:17.211933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1015132
Coefficient of variation (CV)2.2605682
Kurtosis7.3788131
Mean0.48727273
Median Absolute Deviation (MAD)0
Skewness2.7400254
Sum2680
Variance1.2133313
MonotonicityNot monotonic
2023-07-17T14:35:17.287313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4203
76.4%
1 625
 
11.4%
2 349
 
6.3%
5 158
 
2.9%
3 99
 
1.8%
4 63
 
1.1%
6 3
 
0.1%
ValueCountFrequency (%)
0 4203
76.4%
1 625
 
11.4%
2 349
 
6.3%
3 99
 
1.8%
4 63
 
1.1%
5 158
 
2.9%
6 3
 
0.1%
ValueCountFrequency (%)
6 3
 
0.1%
5 158
 
2.9%
4 63
 
1.1%
3 99
 
1.8%
2 349
 
6.3%
1 625
 
11.4%
0 4203
76.4%

credit_risk_score
Real number (ℝ)

Distinct400
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.19473
Minimum-122
Maximum371
Zeros3
Zeros (%)0.1%
Negative52
Negative (%)0.9%
Memory size85.9 KiB
2023-07-17T14:35:17.395140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122
5-th percentile37.95
Q195
median146
Q3214
95-th percentile295
Maximum371
Range493
Interquartile range (IQR)119

Descriptive statistics

Standard deviation79.848905
Coefficient of variation (CV)0.51450785
Kurtosis-0.48627814
Mean155.19473
Median Absolute Deviation (MAD)58
Skewness0.19287276
Sum853571
Variance6375.8477
MonotonicityNot monotonic
2023-07-17T14:35:17.534729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
109 38
 
0.7%
92 36
 
0.7%
99 35
 
0.6%
128 35
 
0.6%
161 34
 
0.6%
104 33
 
0.6%
119 33
 
0.6%
127 32
 
0.6%
120 32
 
0.6%
108 31
 
0.6%
Other values (390) 5161
93.8%
ValueCountFrequency (%)
-122 1
< 0.1%
-119 1
< 0.1%
-97 1
< 0.1%
-91 2
< 0.1%
-87 1
< 0.1%
-85 1
< 0.1%
-80 1
< 0.1%
-79 2
< 0.1%
-78 1
< 0.1%
-76 1
< 0.1%
ValueCountFrequency (%)
371 1
< 0.1%
363 1
< 0.1%
361 1
< 0.1%
357 1
< 0.1%
356 1
< 0.1%
355 2
< 0.1%
354 1
< 0.1%
353 2
< 0.1%
352 1
< 0.1%
351 1
< 0.1%

email_is_free
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
3281 
0
2219 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

Length

2023-07-17T14:35:17.652509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:17.753258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

Most occurring characters

ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3281
59.7%
0 2219
40.3%

housing_status
Real number (ℝ)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2812727
Minimum0
Maximum5
Zeros2093
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:17.835239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2916191
Coefficient of variation (CV)1.0080751
Kurtosis-0.33112101
Mean1.2812727
Median Absolute Deviation (MAD)1
Skewness0.76248663
Sum7047
Variance1.6682799
MonotonicityNot monotonic
2023-07-17T14:35:17.928955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2093
38.1%
2 1592
28.9%
1 1093
19.9%
4 588
 
10.7%
3 126
 
2.3%
5 8
 
0.1%
ValueCountFrequency (%)
0 2093
38.1%
1 1093
19.9%
2 1592
28.9%
3 126
 
2.3%
4 588
 
10.7%
5 8
 
0.1%
ValueCountFrequency (%)
5 8
 
0.1%
4 588
 
10.7%
3 126
 
2.3%
2 1592
28.9%
1 1093
19.9%
0 2093
38.1%

phone_home_valid
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
3631 
1
1869 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%

Length

2023-07-17T14:35:18.023600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:18.110961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%

Most occurring characters

ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3631
66.0%
1 1869
34.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
4774 
0
726 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

Length

2023-07-17T14:35:18.187200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:18.278478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

Most occurring characters

ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4774
86.8%
0 726
 
13.2%

bank_months_count
Real number (ℝ)

Distinct28
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.654545
Minimum-1
Maximum31
Zeros0
Zeros (%)0.0%
Negative1756
Negative (%)31.9%
Memory size85.9 KiB
2023-07-17T14:35:18.379644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median2
Q325
95-th percentile30
Maximum31
Range32
Interquartile range (IQR)26

Descriptive statistics

Standard deviation12.558455
Coefficient of variation (CV)1.1786946
Kurtosis-1.4791053
Mean10.654545
Median Absolute Deviation (MAD)3
Skewness0.50735103
Sum58600
Variance157.71479
MonotonicityNot monotonic
2023-07-17T14:35:18.486558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
-1 1756
31.9%
1 869
15.8%
28 428
 
7.8%
30 399
 
7.3%
15 270
 
4.9%
25 264
 
4.8%
31 257
 
4.7%
10 167
 
3.0%
2 163
 
3.0%
20 143
 
2.6%
Other values (18) 784
14.3%
ValueCountFrequency (%)
-1 1756
31.9%
1 869
15.8%
2 163
 
3.0%
3 31
 
0.6%
4 19
 
0.3%
5 125
 
2.3%
6 71
 
1.3%
9 35
 
0.6%
10 167
 
3.0%
11 97
 
1.8%
ValueCountFrequency (%)
31 257
4.7%
30 399
7.3%
29 41
 
0.7%
28 428
7.8%
27 48
 
0.9%
26 97
 
1.8%
25 264
4.8%
24 19
 
0.3%
22 18
 
0.3%
21 102
 
1.9%

has_other_cards
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
4675 
1
825 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

Length

2023-07-17T14:35:18.586792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:18.677981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4675
85.0%
1 825
 
15.0%

proposed_credit_limit
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean675.24545
Minimum200
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:18.749472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum200
5-th percentile200
Q1200
median200
Q31500
95-th percentile1500
Maximum2000
Range1800
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation592.83664
Coefficient of variation (CV)0.87795724
Kurtosis-0.90572939
Mean675.24545
Median Absolute Deviation (MAD)0
Skewness0.80987137
Sum3713850
Variance351455.28
MonotonicityNot monotonic
2023-07-17T14:35:18.830934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
200 2798
50.9%
1500 1180
21.5%
500 722
 
13.1%
1000 468
 
8.5%
2000 217
 
3.9%
990 56
 
1.0%
510 26
 
0.5%
1900 26
 
0.5%
490 6
 
0.1%
210 1
 
< 0.1%
ValueCountFrequency (%)
200 2798
50.9%
210 1
 
< 0.1%
490 6
 
0.1%
500 722
 
13.1%
510 26
 
0.5%
990 56
 
1.0%
1000 468
 
8.5%
1500 1180
21.5%
1900 26
 
0.5%
2000 217
 
3.9%
ValueCountFrequency (%)
2000 217
 
3.9%
1900 26
 
0.5%
1500 1180
21.5%
1000 468
 
8.5%
990 56
 
1.0%
510 26
 
0.5%
500 722
 
13.1%
490 6
 
0.1%
210 1
 
< 0.1%
200 2798
50.9%

foreign_request
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5292 
1
 
208

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

Length

2023-07-17T14:35:18.929426image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:19.019341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5292
96.2%
1 208
 
3.8%

source
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5452 
1
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

Length

2023-07-17T14:35:19.099783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:19.198348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5452
99.1%
1 48
 
0.9%

session_length_in_minutes
Real number (ℝ)

Distinct5489
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9746752
Minimum-1
Maximum76.362406
Zeros0
Zeros (%)0.0%
Negative12
Negative (%)0.2%
Memory size85.9 KiB
2023-07-17T14:35:19.292722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1.3324554
Q13.2253358
median5.0765253
Q38.7958365
95-th percentile25.614058
Maximum76.362406
Range77.362406
Interquartile range (IQR)5.5705007

Descriptive statistics

Standard deviation9.0229966
Coefficient of variation (CV)1.1314563
Kurtosis11.869599
Mean7.9746752
Median Absolute Deviation (MAD)2.3886105
Skewness3.126887
Sum43860.713
Variance81.414468
MonotonicityNot monotonic
2023-07-17T14:35:19.699301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 12
 
0.2%
7.411498314 1
 
< 0.1%
3.275862427 1
 
< 0.1%
8.684210979 1
 
< 0.1%
5.036845712 1
 
< 0.1%
22.84562709 1
 
< 0.1%
7.172830282 1
 
< 0.1%
4.053199687 1
 
< 0.1%
4.883144216 1
 
< 0.1%
3.845947956 1
 
< 0.1%
Other values (5479) 5479
99.6%
ValueCountFrequency (%)
-1 12
0.2%
0.1325048248 1
 
< 0.1%
0.2202849859 1
 
< 0.1%
0.2548482044 1
 
< 0.1%
0.3140666018 1
 
< 0.1%
0.3457528061 1
 
< 0.1%
0.3538600906 1
 
< 0.1%
0.4043373105 1
 
< 0.1%
0.4088647214 1
 
< 0.1%
0.4194866812 1
 
< 0.1%
ValueCountFrequency (%)
76.36240586 1
< 0.1%
72.72224212 1
< 0.1%
69.40569088 1
< 0.1%
68.09678888 1
< 0.1%
67.37160605 1
< 0.1%
67.03353514 1
< 0.1%
65.77706913 1
< 0.1%
64.96949928 1
< 0.1%
64.60134564 1
< 0.1%
63.62165289 1
< 0.1%

device_os
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
3
2303 
2
1457 
0
1327 
1
365 
4
 
48

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%

Length

2023-07-17T14:35:19.803081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:19.900953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 2303
41.9%
2 1457
26.5%
0 1327
24.1%
1 365
 
6.6%
4 48
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
2931 
1
2569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%

Length

2023-07-17T14:35:19.988364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:20.085852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%

Most occurring characters

ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2931
53.3%
1 2569
46.7%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
1
5134 
2
 
313
0
 
52
-1
 
1

Length

Max length2
Median length1
Mean length1.0001818
Min length1

Characters and Unicode

Total characters5501
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5134
93.3%
2 313
 
5.7%
0 52
 
0.9%
-1 1
 
< 0.1%

Length

2023-07-17T14:35:20.165131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:20.266440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5135
93.4%
2 313
 
5.7%
0 52
 
0.9%

Most occurring characters

ValueCountFrequency (%)
1 5135
93.3%
2 313
 
5.7%
0 52
 
0.9%
- 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
> 99.9%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5135
93.4%
2 313
 
5.7%
0 52
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5501
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5135
93.3%
2 313
 
5.7%
0 52
 
0.9%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5501
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5135
93.3%
2 313
 
5.7%
0 52
 
0.9%
- 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size85.9 KiB
0
5500 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5500
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5500
100.0%

Length

2023-07-17T14:35:20.351536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-17T14:35:20.445389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 5500
100.0%

Most occurring characters

ValueCountFrequency (%)
0 5500
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5500
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5500
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5500
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5500
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5500
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5500
100.0%

month
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4230909
Minimum0
Maximum7
Zeros734
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size85.9 KiB
2023-07-17T14:35:20.510718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.273309
Coefficient of variation (CV)0.66411002
Kurtosis-1.2050631
Mean3.4230909
Median Absolute Deviation (MAD)2
Skewness0.029229699
Sum18827
Variance5.1679337
MonotonicityNot monotonic
2023-07-17T14:35:20.592366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 771
14.0%
0 734
13.3%
4 701
12.7%
1 680
12.4%
6 665
12.1%
5 664
12.1%
2 655
11.9%
7 630
11.5%
ValueCountFrequency (%)
0 734
13.3%
1 680
12.4%
2 655
11.9%
3 771
14.0%
4 701
12.7%
5 664
12.1%
6 665
12.1%
7 630
11.5%
ValueCountFrequency (%)
7 630
11.5%
6 665
12.1%
5 664
12.1%
4 701
12.7%
3 771
14.0%
2 655
11.9%
1 680
12.4%
0 734
13.3%

Interactions

2023-07-17T14:35:10.583900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:25.426105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.594532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:31.373118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:34.035013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:36.420257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.922215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.607399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.947164image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:46.585759image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.989372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:51.378173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:54.059000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:56.062493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:58.043150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:00.207585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:02.127824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:04.078471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:06.429499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:08.488636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.685531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:25.589779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.755436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:31.519684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:34.158388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:36.559367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:39.058015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.717288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:44.075631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:46.704421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:49.110890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:51.504220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:54.181302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:56.165499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:58.140054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:00.313874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:02.224500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
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2023-07-17T14:35:07.600460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:09.637375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.096242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:27.513032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:30.377978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.108190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:35.472350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:37.881072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:40.658087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.007924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.397603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.024579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:50.452260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:52.856314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.331657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.255332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.472020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.357927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.320681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:05.633730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:07.688670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:09.731964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.211297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:27.633303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:30.524948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.228191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:35.594613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.023674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:40.776904image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.120895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.522763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.146562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:50.568345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.240860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.426720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.357912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.566664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.456834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.422217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:05.729155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:07.797711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:09.833657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.336124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:27.748522image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:30.644593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.349577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:35.708939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.155582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:40.895821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.233706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.638229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.262065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:50.683220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.356179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.517490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.459162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.656550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.547467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.515691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:05.820905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:07.898480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:09.937236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.445546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:27.877443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:30.754141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.459901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:35.817020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.264220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.006797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.340632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.738015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.378238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:50.786715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.466389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.601865image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.564033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.744009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.629663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.602649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:05.908862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:07.987458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.049163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.546112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.018157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:30.876344image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.570958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:35.931616image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.391487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.115644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.452922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.845525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.502220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:50.895892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.576015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.689481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.654711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.830965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.716377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.692988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:05.998896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:08.078658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.146228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.637590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.141305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:31.000950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.681744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:36.047879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.529906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.226219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.573225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:45.954967image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.626238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:51.008220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.690830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.781801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.747567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:59.922388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.808316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.782667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:06.096781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:08.177144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.245789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.739295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.284998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:31.121375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.798812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:36.168096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.656769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.362830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.698159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:46.071770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.743277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:51.129517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.809253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.872754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.846292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:00.016109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:01.921981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.881134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:06.210829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:08.276739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.355026image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:12.845351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:28.441728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:31.243913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:33.919647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:36.291507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:38.793307image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:41.486825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:43.822849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:46.190049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:48.867289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:51.252132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:53.932809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:55.969554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:34:57.946286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:00.114219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:02.035450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:03.979117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:06.328686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:08.386339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-17T14:35:10.473474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Missing values

2023-07-17T14:35:13.021972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-17T14:35:13.417407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
248010.60.113599-154300.015598-1.546016381612185.5047816087.2495785990.732491165521011110200.0107.41149800100
560610.80.479826-1198500.027031-0.93026239077417.8797255875.2753085420.21818367019610011501500.00033.17827330101
282600.10.128738-1115500.031019-0.72459619986907.8611045759.1456484777.0741511030451101300200.0002.25804331103
610600.70.3679063118201.326119-1.802038215906730.8206674624.6813224194.81102911311391201-10200.0007.22759520105
558600.10.479825567200.015002-0.957448216761886.3453822324.1584053065.0457341902231401-101500.00010.94751720107
932600.90.453888-120400.01835848.58876202453839.5908893998.5182934039.27687912701291301210500.0007.66389730106
612410.90.603019-199400.002533-1.259496114593416.7889205000.6443834240.3885172341111560001280200.0008.97758630105
262600.40.4315261624300.008287-1.030496238906470.5490764844.9085635132.77119501101031101-10200.00034.08624501102
956600.30.239957-189300.016323-1.3743362274912927.8145845197.7514445491.9672451801350101-11500.0005.00447321101
349300.70.821453-184300.019228-0.33050737859285.3229517158.5698495694.9461219902400201601500.0004.23824531101
fraud_boolincomename_email_similarityprev_address_months_countcurrent_address_months_countcustomer_agedays_since_requestintended_balcon_amountpayment_typezip_count_4wvelocity_6hvelocity_24hvelocity_4wbank_branch_count_8wdate_of_birth_distinct_emails_4wemployment_statuscredit_risk_scoreemail_is_freehousing_statusphone_home_validphone_mobile_validbank_months_counthas_other_cardsproposed_credit_limitforeign_requestsourcesession_length_in_minutesdevice_oskeep_alive_sessiondevice_distinct_emails_8wdevice_fraud_countmonth
184010.80.181918-1285400.007684-0.310258021727226.1298346203.9132905000.4804291801331001100500.0009.47414130103
9900.10.922070-1250200.01108049.3549440167010643.4934874581.6401905637.164902138018704012001500.0003.77138630102
631510.70.214435-146400.019335-0.959823213925939.0179493943.9290005031.4543070411210210-10200.0004.12532231104
667410.40.120331-1100403.354596-1.00614927615170.1689155586.4762594257.5731451902491011-101500.001-1.00000020105
80310.80.551622-1167500.000003-1.012498231447677.6412425167.6391075429.0721901601630101-101500.0007.85698830201
320500.90.617591-1376500.016545-0.775423226426695.1665225363.7589075037.8087911612340001-111500.0002.85919310103
416400.50.443441-1189500.00169349.882065020297776.6076886518.6695355212.09556726811480111261500.0001.91919300102
456710.40.214503-153500.026847-1.705240113926566.2321705653.9023724467.88612219701170101190200.00029.32064330104
825910.60.372818-1182400.00857449.574227012586578.2558605456.1089044811.6088336957031612011101500.00021.99955030103
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